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on Computational Economics |
| By: | Challoumis, Constantinos |
| Abstract: | While some may view artificial intelligence as a contemporary phenomenon, its roots sink deep into the annals of human ingenuity. Central to the understanding of this domain is the distinction between artificial intelligence (AI) and machine learning (ML). AI manifests as a complex branch of computer science that endeavors to emulate human cognitive functions, thereby enabling machines to perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. On the other hand, machine learning is a subset of AI, focusing primarily on the development of algorithms that allow computers to learn from and make predictions based on data. As data accumulates, these algorithms enhance their performance autonomously—without explicit programming, symbolizing a fundamental shift in our interaction with technology. |
| Keywords: | AI revolution, monetary landscape, job opportunities |
| JEL: | F00 H0 Z0 |
| Date: | 2024–11–15 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122514 |
| By: | Xianhua Peng; Steven Kou; Lekang Zhang |
| Abstract: | We propose a machine learning algorithm for solving finite-horizon stochastic control problems based on a deep neural network representation of the optimal policy functions. The algorithm has three features: (1) It can solve high-dimensional (e.g., over 100 dimensions) and finite-horizon time-inhomogeneous stochastic control problems. (2) It has a monotonicity of performance improvement in each iteration, leading to good convergence properties. (3) It does not rely on the Bellman equation. To demonstrate the efficiency of the algorithm, it is applied to solve various finite-horizon time-inhomogeneous problems including recursive utility optimization under a stochastic volatility model, a multi-sector stochastic growth, and optimal control under a dynamic stochastic integration of climate and economy model with eight-dimensional state vectors and 600 time periods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08668 |
| By: | Qianggang Ding; Haochen Shi; Bang Liu |
| Abstract: | The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00782 |
| By: | Kassiani Papasotiriou; Srijan Sood; Shayleen Reynolds; Tucker Balch |
| Abstract: | Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns, specially when incorporating financial fundamentals. While integrating news data improves short-term performance, substituting detailed news summaries with sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias. Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible and efficient framework for generating accurate stock ratings, serving as a cost-effective alternative to traditional methods. Future work will extend to longer timeframes, incorporate diverse data, and utilize newer models for enhanced insights. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00856 |
| By: | Kaushal Attaluri; Mukesh Tripathi; Srinithi Reddy; Shivendra |
| Abstract: | Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05788 |
| By: | Nane Davtyan |
| Abstract: | The rapid advancement of Artificial Intelligence (AI) has revolutionized consumer behavior analysis and digital marketing strategies by enabling personalized and efficient data-driven approaches. AI-driven tools like predictive analytics, natural language processing (NLP), machine learning, and programmatic advertising allow marketers to process vast amounts of real-time consumer data, facilitating optimized campaign performance and precise targeting. This paper explores the integration of AI in marketing, highlighting its role in enhancing predictive analytics, sentiment analysis, and real-time segmentation. Compared to traditional methods, AI-driven insights significantly improve engagement, accuracy, and return on investment (ROI). AI also plays a vital role in marketing automation, allowing dynamic adjustments in campaigns, ad placements, and content creation, improving efficiency and reducing costs. However, AI’s reliance on consumer data raises concerns regarding data privacy and algorithmic bias, especially in targeting. This paper stresses the importance of ensuring transparency, fairness, and regular audits in AI systems to maintain consumer trust and promote ethical AI use. Future research directions are discussed, focusing on enhancing transparency and algorithmic accountability while navigating the ethical challenges of AI in marketing. |
| Keywords: | Artificial Intelligence (AI), Consumer behavior analysis, Digital marketing, Predictive analytics, Natural language processing (NLP) |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfv:sbsrec:005 |
| By: | Harris Borman; Anna Leontjeva; Luiz Pizzato; Max Kun Jiang; Dan Jermyn |
| Abstract: | Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavioural impacts of personality in less obvious areas such as investment attitudes or creative decision making. In this study, we investigated whether an LLM persona with a specific Big Five personality profile would perform an investment task similarly to a human with the same personality traits. We used a simulated investment task to determine if these results could be generalised into actual behaviours. In this simulated environment, our results show these personas produced meaningful behavioural differences in all assessed categories, with these behaviours generally being consistent with expectations derived from human research. We found that LLMs are able to generalise traits into expected behaviours in three areas: learning style, impulsivity and risk appetite while environmental attitudes could not be accurately represented. In addition, we showed that LLMs produce behaviour that is more reflective of human behaviour in a simulation environment compared to a survey environment. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05801 |
| By: | Anika Tahsin Meem; Mst. Shapna Akter; Deponker Sarker Depto; M. R. C. Mahdy |
| Abstract: | In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13615 |
| By: | Aleksandr Simonyan |
| Abstract: | This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address the unique challenges posed by highly volatile financial markets. The primary contribution of this work lies in demonstrating the effectiveness of combining time series representation learning with LLM prediction frameworks. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06076 |
| By: | Shuoling Liu; Gaoguo Jia; Yuhang Jiang; Liyuan Chen; Qiang Yang |
| Abstract: | Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of LLMs. Despite the remarkable success of zero-shot Chain-of-Thoughts (CoT) in solving general reasoning tasks, the potential of these methods still remains paid limited attention in the financial reasoning task.To address this issue, we explore multiple prompt strategies and incorporated semantic news information to improve LLMs' performance on financial reasoning tasks.To the best of our knowledge, we are the first to explore this important issue by applying ChatGPT to the gold investment.In this work, our aim is to investigate the financial reasoning capabilities of LLMs and their capacity to generate logical and persuasive investment opinions. We will use ChatGPT, one of the most powerful LLMs recently, and prompt engineering to achieve this goal. Our research will focus on understanding the ability of LLMs in sophisticated analysis and reasoning within the context of investment decision-making. Our study finds that ChatGPT with CoT prompt can provide more explainable predictions and overcome behavioral biases, which is crucial in finance-related tasks and can achieve higher investment returns. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13599 |
| By: | Neha Sanjay Ahuja |
| Abstract: | Several organizations are directing Artificial Intelligence (AI) driven expertise to assist and analyze data insights, identify gaps, and transform their decision-making proficiency, especially under high pressure and tight timelines. This research article investigates the impact of AI on decision-making and its consequences for individuals, companies, and society. Decision-making is crucial for achieving organizational goals. Accurate data, reports, and decisions enhance business predictions, transform strategies, enable timely mid-stage implementation reviews, facilitate quick decisions, boost productivity, and lead businesses toward success and future growth. This article also examines how AI transforms internal operations across various departments, from transport to consultation management, predicting demands, adjusting supply and data levels, optimizing results, and reducing operating costs. AI software enables data-driven choices, improves customer targeting, enhances development, and customizes reports for precise strategic decisions. The article provides an overview of AI mechanisms such as automation efficiency and complex management for multilayered issues beyond human capacity, succession planning, and risk monitoring, benefiting departments like HR, Finance, Sales, and Marketing, and industries including Healthcare, Finance, Consulting, Transportation, and Food & Beverage, as well as government authorities in process automation. Various technologies and tools globally facilitate data-driven decision-making. This article highlights the positive impact of AI on management operations and company success while acknowledging that incorrect decisions may disappoint organizations. As AI enhances decision-making, challenges like ethical concerns, algorithmic biases, social implications, and the Human-AI partnership need addressing. Data privacy, transparency, accountability, and explainability are essential for reputation management. Companies must prioritize ethical AI practices and transparency, ensuring unbiased algorithms. This article focuses on governance, regulations, and policies to mitigate biases and ensure AI aligns with organizational goals, with an emphasis on improving AI functionality. |
| Keywords: | Artificial Intelligence, transformation, complex management, quick business decision, timelines, increase productivity, cost reduction, business success, augmentation, accuracy, ethical and social concern, data protection, transparency, bias, Government regulations |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfv:sbsrec:001 |
| By: | Li Zhanfeng (Zhongnan University of Economics and Law) |
| Abstract: | Mediation models with censored outcomes play a crucial role in social and medical sciences. However, the inherent censoring characteristics of the data often lead existing models to rely on assumptions of linearity, homogeneity, and normality for estimation. Unfortunately, these assumptions may not align with the complexities of real-world problems, limiting the persuasiveness of causal analyses. In this study, I investigate causal mediation analysis within a counterfactual framework by framing it as a neural style transfer problem commonly encountered in image processing. Acknowledging the impressive capabilities of generative adversarial networks (GANs) in handling neural style transfer, I propose a novel GAN-based model named generative adversarial censored mediation network to address mediation issues under my concern. My model employs recti |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:08 |
| By: | Krishna Neupane; Igor Griva |
| Abstract: | According to The Exchange Act, 1934 unlawful insider trading is the abuse of access to privileged corporate information. While a blurred line between "routine" the "opportunistic" insider trading exists, detection of strategies that insiders mold to maneuver fair market prices to their advantage is an uphill battle for hand-engineered approaches. In the context of detailed high-dimensional financial and trade data that are structurally built by multiple covariates, in this study, we explore, implement and provide detailed comparison to the existing study (Deng et al. (2019)) and independently implement automated end-to-end state-of-art methods by integrating principal component analysis to the random forest (PCA-RF) followed by a standalone random forest (RF) with 320 and 3984 randomly selected, semi-manually labeled and normalized transactions from multiple industry. The settings successfully uncover latent structures and detect unlawful insider trading. Among the multiple scenarios, our best-performing model accurately classified 96.43 percent of transactions. Among all transactions the models find 95.47 lawful as lawful and $98.00$ unlawful as unlawful percent. Besides, the model makes very few mistakes in classifying lawful as unlawful by missing only 2.00 percent. In addition to the classification task, model generated Gini Impurity based features ranking, our analysis show ownership and governance related features based on permutation values play important roles. In summary, a simple yet powerful automated end-to-end method relieves labor-intensive activities to redirect resources to enhance rule-making and tracking the uncaptured unlawful insider trading transactions. We emphasize that developed financial and trading features are capable of uncovering fraudulent behaviors. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13564 |
| By: | Dawei Cheng; Yao Zou; Sheng Xiang; Changjun Jiang |
| Abstract: | The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05815 |
| By: | Leogrande, Angelo |
| Abstract: | L'articolo esplora l'importanza strategica dell'implementazione dei servizi di kitting e assembly per affrontare problematiche di assegnazione delle risorse in un magazzino operante nel settore idrotermosanitario. Si concentra sulla crescente complessità delle operazioni logistiche in un contesto caratterizzato da una domanda sempre più personalizzata e dalla necessità di garantire tempi di consegna rapidi. Attraverso un'analisi approfondita, il lavoro evidenzia come il kitting e l'assembly siano strumenti fondamentali per ottimizzare i flussi operativi, migliorare l'efficienza e soddisfare le aspettative dei clienti. Il kitting viene descritto come il processo di raggruppamento di componenti per assemblaggi specifici, contribuendo alla riduzione dei tempi operativi e minimizzando gli errori umani. L'assembly, d'altro canto, completa il ciclo producendo kit semi-finiti o finiti, pronti per la distribuzione. L'articolo analizza il valore di questa integrazione, mostrando come essa migliori la gestione degli spazi e la tracciabilità dei materiali, oltre a fornire un vantaggio competitivo. La ricerca adotta un approccio olistico, prendendo in esame sia gli aspetti tecnologici, come l’uso di software di gestione logistico avanzato, sia quelli collaborativi, evidenziando l'importanza del coordinamento tra risorse umane e materiali. Inoltre, include casi studio dettagliati che dimostrano i benefici tangibili delle soluzioni implementate, come la riduzione degli errori, l’aumento dell’efficienza e un impatto positivo sulla sostenibilità. Questo lavoro rappresenta un contributo significativo per le aziende che intendono migliorare la gestione logistica, con un focus su innovazione e ottimizzazione dei processi. |
| Keywords: | Kitting, Assembly, Idrotermosanitario, Machine Learning Regressions, Machine Learning Clustering. |
| JEL: | L9 L90 L91 L92 L93 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122746 |
| By: | Zuo Xiangtai (Xiamen University) |
| Abstract: | With the development of the times and the advancement of technology, general statistical data has been widely used. At the same time, unstructured data in the form of text is gradually becoming the backbone of the empirical |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:14 |
| By: | Yan-Feng Wu; Jian-Qiang Hu |
| Abstract: | We introduce ajdmom, a Python package designed for automatically deriving moment formulas for the well-established affine jump diffusion (AJD) processes. ajdmom can produce explicit closed-form expressions for moments or conditional moments of any order, significantly enhancing the usability of AJD models. Additionally, ajdmom can compute partial derivatives of these moments with respect to the model parameters, offering a valuable tool for sensitivity analysis. The package's modular architecture makes it easy for adaptation and extension by researchers. ajdmom is open-source and readily available for installation from GitHub or the Python package index (PyPI). |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06484 |
| By: | Orhan Erdem; Kristi Hassett; Feyzullah Egriboyun |
| Abstract: | We evaluate the reliability of two chatbots, ChatGPT (4o and o1-preview versions), and Gemini Advanced, in providing references on financial literature and employing novel methodologies. Alongside the conventional binary approach commonly used in the literature, we developed a nonbinary approach and a recency measure to assess how hallucination rates vary with how recent a topic is. After analyzing 150 citations, ChatGPT-4o had a hallucination rate of 20.0% (95% CI, 13.6%-26.4%), while the o1-preview had a hallucination rate of 21.3% (95% CI, 14.8%-27.9%). In contrast, Gemini Advanced exhibited higher hallucination rates: 76.7% (95% CI, 69.9%-83.4%). While hallucination rates increased for more recent topics, this trend was not statistically significant for Gemini Advanced. These findings emphasize the importance of verifying chatbot-provided references, particularly in rapidly evolving fields. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.07031 |